Much work has been performed in the area of text document classification. For example, e-mail sorting has been proposed in Sahami, Dumais, Heckerman & Horvitz, “A Bayesian Approach to Filtering Junk E-Mail,” Learning For Text Categorization: Papers from the 1988 Workshop, AAAI Technical Report WS-98-05 (1998) and Cohen, Carvalho & Mitchell, “Learning to Classify Email in ‘Speech Acts,’” EMNLP 2004, each of which is incorporated herein by reference in its entirety.
Text-document classification is also performed by Internet-based search engines, such as are described in Joachims, “Optimizing Search Engines Using Clickthrough Data,” Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (2002) and McCallum, Nigam, Rennie & Seymore, “Building Domain-Specific Search Engines with Machine Learning Techniques,” AAAI-99 Spring Symposium, each of which is incorporated herein by reference in its entirety.
Other work teaches the classification of news articles, such as Allen, Carbonell, Doddington, Yamron & Yang, “Topic Detection and Tracking Pilot Study: Final Report,” Proceedings of the Broadcast News Transcription and Understanding Workshop, pp 194-218 (1998) and Billsus & Pazzani, “A Hybrid User Model for News Story Classification,” Proceedings of the Seventh International Conference on User Modeling (UM '99), Banff Canada (Jun. 20-24, 1999), each of which is incorporated herein by reference in its entirety.
Moreover, information in medical reports can be classified by text documentation classifiers, such as those taught by Hripcsak, Friedman, Alderson, DuMouchel, Johnson & Clayton, “Unlocking Clinical Data from Narrative Reports: A Study of Natural Language Processing,” Ann Intern Med 122(9): 681-88 (1995); and Wilcox & Hripcsak, “The Role of Domain Knowledge in Automating Medical Text Report Classification,” Journal of the American Medical Information Association 10:330-38 (2003), each of which is incorporated herein by reference in its entirety.
In addition, research has been performed in the area of automated essay scoring, such as by Page, “The Imminence of Grading Essays by Computer,” Phi Delta Kappan 48:238-43 (1966); Burstein et al., “Automated Scoring Using a Hybrid Feature Identification Technique,” Proceedings of 36th Annual Meeting of the Association of Computational Linguistics, pp 206-10 (1998); Foltz, Kintsch & Landauer, “Analysis of Text Coherence Using Latent Semantic Analysis,” Discourse Processes 25(2-3): 285-307 (1998); Larkey, “Automatic Essay Grading Using Text Categorization Techniques,” Proceedings of the 21st ACM-SIGIR Conference on Research and Development in Information Retrieval, pp 90-95 (1998); and Elliott, “Intellemetric: From Here to Validity,” in Shermis & Berstein, eds., “Automated Essay Scoring: A Cross-Disciplinary Perspective” (2003), each of which is incorporated herein by reference in its entirety.
In the area of automated essay evaluation and scoring, systems have been developed that perform one or more natural language processing (“NLP”) methods. For example, a first NLP method might include a scoring application that extracts linguistic features from an essay and uses a statistical model of how these features are related to overall writing quality in order to assign a ranking or score to the essay. A second NLP method might include an error evaluation application that evaluates errors in grammar, usage and mechanics, identifies and essay's discourse structure, and recognizes undesirable stylistics features.
Additional NLP methods can provide feedback to essay writers regarding whether an essay appears to be off-topic. In this context, an off-topic essay is an essay that pertains to a different subject than other essays in a training corpus, as determined by word usage. Such methods presently require the analysis of a significant number of essays that are written to a particular test question (i.e., a “prompt”) and have been previously scored by a human reader to be used for training purposes.
One such method for determining if an essay is off-topic requires calculating two values determined based on the vocabulary used in an essay. In the method, a “z-score” is computed for each essay for each of two variables: a) a relationship between the words in the essay response and the words in a set of training essays written in response to the prompt (essay question) to which the essay responds, and b) a relationship between the words in the essay response and the words in the text of the essay prompt. A z-score value indicates an essay's relationship to the mean and standard deviation values of a particular variable based on a training corpus of human-scored essay data from which off-topic essays are excluded.
In order to identify off-topic essays, z-scores are computed for: a) the maximum cosine value, which is the highest cosine value among all cosines between an essay and all training essays, and b) the prompt cosine value, which is the cosine value between and essay and the text of the essay prompt. When a z-score exceeds a predefined threshold, the essay is likely to be anomalous (i.e., “off-topic”), since the threshold is typically set to a value representing an acceptable distance from the mean. These values can be used in an advisory feature set.
The equation for calculating a z-score for a particular essay is
  z  =                    Value        -        Mean                    Std        .                                  ⁢        Dev        .              .  The mean and the standard deviation can relate to the maximum cosine value or the prompt cosine value. Z-score values can be used to determine, for example, the overly repetitious use of particular words in an essay and/or whether an essay is off-topic.
The accuracy of such an approach can be determined by examining the false positive rate and the false negative rate. The false positive rate is the percentage of appropriately written, on-topic essays that have been incorrectly identified as off-top essays. The false negative rate is the percentage of off-topic essays that have been incorrectly identified as on-topic. Typically, it is preferable to have a lower false positive rate so that a student is not incorrectly admonished for writing an off-topic essay. For a typical essay set, the false positive rate using this method is approximately 7%, and the false negative rate is approximately 33%.
What is needed is a method of determining vocabulary similarity for an essay with respect to the prompt to which the essay is answered which reduces the false positive and false negative error rates.
The disclosed embodiments are directed to solving one or more of the above-listed problems.